1. Field of Art
The invention generally relates to video processing, and more specifically to video fingerprinting.
2. Description of the Related Art
Online systems store, index, and make available for consumption various forms of media content to Internet users. This content may take a variety of forms; in particular, video content, including streaming video is widely available across the Internet. Online video systems allow users to view videos uploaded by other users. Popular online content systems for videos include YouTube™. These online video systems may contain thousands or millions of video files, making management of these video files an extremely challenging task. One challenge is that users upload unauthorized copies of copyrighted video content since online video systems allow users to freely upload video content. As such, online video systems need a mechanism for identifying and removing these unauthorized copies.
While some files may be identified by file name or other information provided by the user, this identification information may be incorrect or insufficient to correctly identify the video. An alternate approach of using humans to manually identifying video content is expensive and time consuming. Various methods have been used to automatically detect similarities between video files based on their video content. In the past, various identification techniques (such as an MD5 hash on the video file) have been used to identify exact copies of video files. Generally, a digital “fingerprint” is generated by applying a hash-based fingerprint function to a bit sequence of the video file; this generates a fixed-length monolithic bit pattern—the fingerprint—that uniquely identifies the file based on the input bit sequence. Then, fingerprints for files are compared in order to detect exact bit-for-bit matches between files. Alternatively, instead of computing a fingerprint for the whole video file, a fingerprint can be computed for only the first frame of video, or for a subset of video frames.
However, these methods often fail to identify unauthorized videos that include other content that is specifically added to disguise unauthorized content. For example, users place video frames of an unauthorized video in cinema or monochrome surroundings to give an impression that it is a different video to avoid being detected. Accordingly, an improved technique is needed for finding similarities between videos and detecting unauthorized content based on the perceived visual content of the video.